57-19 Using Structured Decision Making and Adaptive Management to Reduce Critical Uncertainties in Water Resource Decisions: Examples from ACF Basin

James T. Peterson , Department of Fisheries and Wildlife, Oregon State University, USGS Oregon Cooperative Fish and Wildlife Research Unit, Corvallis, OR
Mary C. Freeman , USGS Patuxent Wildlife Research Center, Athens, GA
Jacob H. LaFontaine , USGS Georgia Water Science Center, Atlanta, GA
Robert B. Jacobson , USGS Columbia Environmental Research Center, Columbia, MO
Caroline M. Elliott , Columbia Environmental Research Center, U.S. Geological Survey, Columbia, MO
Lauren Hay , USGS National Research Program, Lakewood, CO
John W. Jones , USGS Eastern Geographic Science Center, Reston, VA
River regulation, water use, and land development are among the foremost problems faced by aquatic resource managers. Identifying and quantifying their effects on aquatic communities is crucial for effective water resources decision-making. In the Apalachicola, Chattahoochee, and Flint (ACF) river basins, increasing demand for water from the steady growth of the Atlanta Metropolitan Area and increased agricultural irrigation in the Coastal Plain has the potential to alter streamflows throughout the basin. We developed a landscape-scale approach to predict the effects of flow alteration and stream fragmentation on the distribution and persistence of fish communities. The approach models the dynamics of fish communities in individual stream segments using empirical estimates of meta-demographic rates (colonization, extinction, reproduction) for functional guilds. The model operates on an annual time step and requires several estimated inputs: seasonal streamflows, geomorphic channel characteristics, upslope land use, and stream size for estimating meta-demographic rates in each segment. These ecological models were linked to hydrologic models to predict the persistence of fish species under future scenarios of flow alteration and impoundment. Sensitivity analysis indicated that estimates of the effects of water use and impoundment were strongly influenced by the assumptions about fish population dynamics. While it is clear that seasonal streamflows affect fish populations, remaining uncertainty about biological system dynamics make it difficult to predict with certainty the effects of water management decisions. To improve future water resource decision-making, we developed an adaptive framework, within which targeted monitoring data are used to iteratively improve model components and our understanding of the mechanisms linking land use, hydrology, and aquatic biota.